This subject matter disclosed herein relates generally to data reconstruction systems and methods, and more particularly to systems and methods to identify and suppress artifacts in soft-field tomography.
Soft-field tomography, such as Electrical Impedance Spectroscopy (EIS) (also referred to as Electrical Impedance Tomography (EIT)), diffuse optical tomography, elastography, and related modalities may be used to measure the internal properties of an object, such as the electrical properties of materials comprising internal structures of an object (e.g., a region of a human body). For example, in EIS systems, an estimate is made of the distribution of electrical conductivities of the internal structures. Such EIS systems reconstruct the conductivity and/or permittivity of the materials within the area or volume based on an applied excitation (e.g., current) and a measured response (e.g., voltage) acquired at a surface of the area or volume. Visual distributions of the estimates can then be formed.
In EIS, the complex conductivity distributions within a volume are determined using assumed known applied electrical excitations, apriori geometry and surface electrode data, and signal measurement data from transducers coupled to the volume under test. An electromagnetic model with assumptions about the volume and electrode geometry, boundary conditions, the applied excitation, and the interior conductivity distribution are then used to determine a predicted response to a given excitation. The inverse problem in EIS is to determine the spatial distribution of complex conductivities that give rise to the difference between measured data and the predicted model data.
The EIS inverse problem is highly ill-posed in that large perturbations in the conductivity distribution may result in small changes in the measurement data. Similarly, small changes or errors in the applied excitation may result in large changes in the measured data. The solution to the inverse problem is the complex conductivity distribution, within the assumed volume and electrode geometry, which accounts for differences in the measured data from the data predicted by a forward model. In addition to conductivity distribution differences, differences between modeled and experimental excitation, differences between modeled and experimental surface geometry, and differences between electrode size, position, arrangement, among others can also account for the differences between prediction data and measured data.
Thus, EIS reconstructions of conductivity distributions may inherently suffer from artifacts due to experimental geometry and electrode mismatch to the forward prediction model.